Introduction

Due to the rapid spread of COVID-19, multiple nations have suffered large infection rates, many of which have been recorded, along with specific information on when the cases were first observed, in addition to status on the outcomes of each patient. This data is what we will be basing our research on.

Summary Information

country with most cases country with least cases 3 more points of interest Notable points of interest in the dataset are that ‘r’ contains the most confirmed cases in the world, with ‘r’ containing the least.

Table

A table was included because it was the most clear way to display specific numeric values, and in this case, was used to show the twenty countries with the most recorded deaths. This table reveals how the US leads the world in number of deaths by a large margin. By listing out the results by number of deaths, we can more easily see the countries that have been most affected by COVID-19, and additional information for those countries

## Selecting by Total Deaths
Country Confirmed Cases Recovered Cases Total Deaths
US 31464810 3460788 1684929
Italy 8057805 2211736 1025162
Spain 7952504 3282730 833391
China 7442665 5428530 305817
Germany 6039054 3480471 183272
France 5680108 1471182 737512
United Kingdom 4984308 25348 735428
Iran 3687232 2330420 232795
Turkey 3325094 1031075 82193
Brazil 2166180 844888 141527
Canada 1549581 544413 78829
Belgium 1538687 339568 208917
Netherlands 1321384 8894 147309
Switzerland 1193056 677483 54760
India 905084 215556 29599
Portugal 814581 36565 28458
Sweden 633667 45730 66285
Ecuador 604798 51466 26838
Ireland 586373 236833 29121
Mexico 477103 240781 42669

Map

This map displays the number of cases per a country. We included this chart because it very clearly displays how the number of COVID19 cases is dispersed throughout the world. As seen below, this graphic reveals that the number of cases in the U.S. far out weighs the number of cases elsewhere.

## Reading layer `countries' from data source `C:\Users\hubhu\Documents\A1 Q3 2019\INFO 201\code\final-project-jzli23\data\countries.geojson' using driver `GeoJSON'
## Simple feature collection with 255 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## geographic CRS: WGS 84
## Warning: Column `Country`/`ADMIN` joining character vector and factor, coercing
## into character vector
## Reading layer `countries' from data source `C:\Users\hubhu\Documents\A1 Q3 2019\INFO 201\code\final-project-jzli23\data\countries.geojson' using driver `GeoJSON'
## Simple feature collection with 255 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## geographic CRS: WGS 84
## Warning: Column `Country`/`ADMIN` joining character vector and factor, coercing
## into character vector

Pie Chart

The pie chart below shows the number of deaths due to COVID-19 by continent. It was chosen because it clearly shows the proportions of death in each sector of the circle, as each color represents a different continent. The quadrant shown besides the pie chart was also added to make comparisons of proportions more effective.

Bar Chart

The plot below highlights the top 10 countries with the most amount of deaths due to COVID-19. It is in the form of a colored bar chart. This plot displays the x-axis as the amount of deaths and the y-axis as the respective country. This visual effectively displays which states had more/less deaths than one another. Additionally, I added another level of encoding that is color, which further differentiates the difference in amount of deaths by country. I chose these visual types because length encoding allows people to easily identify differences in deaths and although color ranks as a lesser encoding, it adds another level of clarity.